import pandas as pd 
from datetime import datetime, timedelta
import warnings
import yfinance as yf
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta 
from scipy import stats
import matplotlib.dates as mdates
from matplotlib.colors import LinearSegmentedColormap
from plotly import tools
import plotly.tools as tls
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, plot, iplot
import statsmodels.api as sm
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

df1=pd.read_csv(r"C:\Users\PMLS\Downloads\sAND P TOP 5000 COAMPNIES NAMES\sp500_stocks.csv")
df1.head()

df1['Date'] = pd.to_datetime(df1['Date'])

now_time = datetime.now()
five_years_ago = now_time - timedelta(days=5*365)

df = df1[df1['Date'] >= five_years_ago]
df = df.reset_index(drop=True)  
df

# Ensure 'Date' column is in datetime format
df['Date'] = pd.to_datetime(df['Date'])

# Define the start and end dates
start_date = pd.to_datetime('2019-09-09')
end_date = pd.to_datetime('2024-09-04')

# Generate a date range from start_date to end_date
full_date_range = pd.date_range(start=start_date, end=end_date)

# Extract the unique dates from the 'Date' column in df_last_5_years
available_dates = pd.to_datetime(df['Date'].unique())

# Find the missing dates by comparing the full range with available dates
missing_dates = full_date_range.difference(available_dates)

# Get the count of missing dates
missing_count = len(missing_dates)

# Print the count of missing dates
print(f"Count of Missing Dates: {missing_count}")

# Print each missing date in a human-readable format
for missing_date in missing_dates:
    print(f"{missing_date.strftime('%d %B %Y')} is missing")

df=df.rename(columns={"Symbol":"ticks"})
new_df= df.dropna()
new_df.isnull().sum()

Date         0
ticks        0
Adj Close    0
Close        0
High         0
Low          0
Open         0
Volume       0
dtype: int64

new_df

new_df.columns = new_df.columns.str.lower()

mean_dict = {}

# find average of volume traded over a period of time using for loops
for key in new_df['ticks'].unique():
    value = new_df[new_df['ticks'] == key ]['volume'].mean()
    mean_dict[key]= value

print("Length of the mean of ticks dictionary:", len(mean_dict))

# convert dict to pandas dataframe
avaerage_s = pd.Series(mean_dict)
top10_s = avaerage_s.sort_values(ascending=False)[:10]

print("Top 10 company tickers with highest average traded stock volume:\n", top10_s.index)

Length of the mean of ticks dictionary: 503
Top 10 company tickers with highest average traded stock volume:
 Index(['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL'], dtype='object')

def subdataframe(df, tick):
    
    # top 10 ticks
    ticks = list(top10_s.index)
    
    assert tick in ticks, """Stock tick does not belong to top 10 stocks by trade volume, please try any of these:\n
    ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']"""
    
    ndf = new_df[new_df['ticks'] == tick]
    return ndf

sp500Cmp_info_df = pd.read_csv('C:/Users/PMLS/Downloads/sAND P TOP 5000 COAMPNIES NAMES/sp500_companies.csv')
Top10HigestTradeNVD=['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']
info_df=pd.DataFrame({'tick':Top10HigestTradeNVD})
info_df

Final_info = pd.merge(info_df, sp500Cmp_info_df, how='left', left_on='tick', right_on='Symbol')
Final_info =Final_info[['tick','Shortname','Sector','Industry']]
Final_info

# Update company dict for your top 10 companies
company_dict = {
    'NVDA': 'NVIDIA', 
    'TSLA': 'Tesla', 
    'AAPL': 'Apple', 
    'AMZN': 'Amazon', 
    'F': 'Ford Motor Company', 
    'AMD': 'Advanced Micro Devices', 
    'BAC': 'Bank of America', 
    'T': 'AT&T', 
    'AAL': 'American Airlines', 
    'CCL': 'Carnival Corporation'
}

# Create sub-dataframes for each company
nvda_df = subdataframe(new_df, 'NVDA')
tsla_df = subdataframe(new_df, 'TSLA')
aapl_df = subdataframe(new_df, 'AAPL')
amzn_df = subdataframe(new_df, 'AMZN')
f_df = subdataframe(new_df, 'F')
amd_df = subdataframe(new_df, 'AMD')
bac_df = subdataframe(new_df, 'BAC')
t_df = subdataframe(new_df, 'T')
aal_df = subdataframe(new_df, 'AAL')
ccl_df = subdataframe(new_df, 'CCL')

# Define the function to calculate daily return and company name
def dailyfunc(df):
    df['daily return'] = ((df['close'] - df['open']) / df['open']) * 100
    df.style.format('{:.2f}%', subset='daily return')
    df['daily_mean'] = (df['open'] + df['close'] + df['high'] + df['low']) / 4
    df['co_name'] = company_dict[df['ticks'].unique()[0]]
    return df

# Apply dailyfunc to each company's dataframe
nvda_df = dailyfunc(nvda_df)
tsla_df = dailyfunc(tsla_df)
aapl_df = dailyfunc(aapl_df)
amzn_df = dailyfunc(amzn_df)
f_df = dailyfunc(f_df)
amd_df = dailyfunc(amd_df)
bac_df = dailyfunc(bac_df)
t_df = dailyfunc(t_df)
aal_df = dailyfunc(aal_df)
ccl_df = dailyfunc(ccl_df)

# Print the start and end date for each company One more for Confioatnos 
print('\t\tStart Date\t\t\t\t\tEnd Date')
print(f"NVDA\t\t{nvda_df['date'].min()}\t\t\t{nvda_df['date'].max()}")
print(f"TSLA\t\t{tsla_df['date'].min()}\t\t\t{tsla_df['date'].max()}")
print(f"AAPL\t\t{aapl_df['date'].min()}\t\t\t{aapl_df['date'].max()}")
print(f"AMZN\t\t{amzn_df['date'].min()}\t\t\t{amzn_df['date'].max()}")
print(f"F\t\t{f_df['date'].min()}\t\t\t{f_df['date'].max()}")
print(f"AMD\t\t{amd_df['date'].min()}\t\t\t{amd_df['date'].max()}")
print(f"BAC\t\t{bac_df['date'].min()}\t\t\t{bac_df['date'].max()}")
print(f"T\t\t{t_df['date'].min()}\t\t\t{t_df['date'].max()}")
print(f"AAL\t\t{aal_df['date'].min()}\t\t\t{aal_df['date'].max()}")
print(f"CCL\t\t{ccl_df['date'].min()}\t\t\t{ccl_df['date'].max()}")

		Start Date					End Date
NVDA		2019-09-16 00:00:00			2024-09-04 00:00:00
TSLA		2019-09-16 00:00:00			2024-09-04 00:00:00
AAPL		2019-09-16 00:00:00			2024-09-04 00:00:00
AMZN		2019-09-16 00:00:00			2024-09-04 00:00:00
F		2019-09-16 00:00:00			2024-09-04 00:00:00
AMD		2019-09-16 00:00:00			2024-09-04 00:00:00
BAC		2019-09-16 00:00:00			2024-09-04 00:00:00
T		2019-09-16 00:00:00			2024-09-04 00:00:00
AAL		2019-09-16 00:00:00			2024-09-04 00:00:00
CCL		2019-09-16 00:00:00			2024-09-04 00:00:00

def plot_closing_stock_prices(dfs, ncols=2):
    # Calculate the number of rows needed
    nrows = (len(dfs) + ncols - 1) // ncols
    
    # Create the figure and axes for the subplots
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 5 * nrows), facecolor='#f4f4f4')
    fig.subplots_adjust(hspace=0.4, wspace=0.3)
    
    for ax, df in zip(axes.flat, dfs):
        # Calculate the highest stock price and the corresponding date
        high = df['close'].max()
        datetime = df[df['close'] == high]['date'].values[0]  

        # Define color based on the stock ticker
        if df['ticks'].unique()[0] in ['GE', 'F']:
            facecolor = '#ed615c'
        else:
            facecolor = '#4bd659'
        
        # Plot the data
        ax.plot(df['date'], df['close'], color='#0f2113')
        ax.set_title(f"{df['co_name'].unique()[0]} Stock Price", fontsize=16, fontweight='bold')
        ax.set_xlabel("Date", fontsize=12)
        ax.set_ylabel("Daily Closing Stock Price", fontsize=12)
        ax.set_facecolor('#ffffff')  # Set background color for each subplot
        
        # Add annotation for the highest stock price
        ax.annotate(
            f"All time high price during\nfive year period\nwas ${high:.2f}",
            xy=(datetime, high),
            xytext=(datetime, high - 0.1 * high),
            bbox=dict(boxstyle="round", facecolor='#f5d3bf', edgecolor='#d0d5db'),
            arrowprops=dict(facecolor='#f0190a', headlength=25, shrink=0.1)
        )

    # Hide any unused subplots
    for ax in axes.flat[len(dfs):]:
        ax.set_visible(False)

    plt.show()

# Call the function with your dataframes
plot_closing_stock_prices([nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df])

def trade_vol(df):
    # Set the style and color palette using Seaborn
    sns.set(style="whitegrid")
    palette = sns.color_palette("pastel")  # Light, pastel color palette
    
    # x and y coords for average trade volume
    ave_x = df['date'].mean()
    ave_y = df['volume'].mean()
    # y coord for max trade volume
    max_y = df['volume'].max()
    # y coord for min trade volume
    min_y = df['volume'].min()
    
    # Create the figure and axis
    fig, ax = plt.subplots(figsize=(12, 7))
    
    # Plot the trade volume with a smooth line
    ax.plot(df['date'], df['volume'], color=palette[0], linewidth=2.5)
    
    # Set the title and labels with a modern font
    ax.set_title(f"{df['co_name'].unique()[0]} Stock Trade Volume", fontsize=22, fontweight='bold')
    ax.set_xlabel("Date", fontsize=16)
    ax.set_ylabel("Daily Trade Volume", fontsize=16)
    
    # Add horizontal lines for max, min, and average trade volumes
    ax.axhline(y=max_y, linestyle='--', lw=2.5, color=palette[1])
    ax.axhline(y=min_y, linestyle='--', lw=2.5, color=palette[2])
    ax.axhline(y=ave_y, linestyle='--', lw=2.5, color=palette[3])
    
    # Add vertical line for maximum trade volume date
    ax.axvline(x=df[df['volume'] == max_y]['date'].values[0], linestyle='--', lw=2.5, color=palette[4])
    
    # Annotate the plot with average, max, and min trade volumes
    ax.annotate(f"Average Trade Volume {ave_y:,.2f}", 
                xy=(ave_x, ave_y), 
                xytext=(ave_x, ave_y + 0.1 * ave_y),
                bbox=dict(boxstyle="round,pad=0.3", facecolor=palette[5], edgecolor=palette[6]),
                arrowprops=dict(arrowstyle="->", color=palette[6]))
    
    ax.annotate(f"Maximum Trade Volume {max_y:,.2f}", 
                xy=(ave_x, max_y), 
                xytext=(ave_x, max_y - 0.1 * max_y),
                bbox=dict(boxstyle="round,pad=0.3", facecolor=palette[5], edgecolor=palette[6]),
                arrowprops=dict(arrowstyle="->", color=palette[6]))
    
    ax.annotate(f"Minimum Trade Volume {min_y:,.2f}", 
                xy=(ave_x, min_y), 
                xytext=(ave_x, min_y - 0.1 * min_y),
                bbox=dict(boxstyle="round,pad=0.3", facecolor=palette[5], edgecolor=palette[6]),
                arrowprops=dict(arrowstyle="->", color=palette[6]))
    
    # Improve the layout
    plt.tight_layout()
    plt.show()

# Example usage
trade_vol(nvda_df)
trade_vol(tsla_df)
trade_vol(aapl_df)
trade_vol(amzn_df)
trade_vol(f_df)
trade_vol(amd_df)
trade_vol(bac_df)
trade_vol(t_df)
trade_vol(aal_df)
trade_vol(ccl_df)

list_df = [nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df]

# loop through the the list_df to find mini and maxi of each stocks 
mini = [df[df['date'] == df['date'].min()]['close'].values.item() for df in list_df]
maxi = [df[df['date'] == df['date'].max()]['close'].values.item() for df in list_df]

# find list of abosolute difference between both stock price
diff = np.array(maxi) - np.array(mini)

# find the percentage growth
growth = (diff/mini)*100
growth_list = growth.tolist()
co_name_list = [df['co_name'].unique()[0] for df in list_df]

# visualize the growth of the stocks
fig, ax = plt.subplots(figsize=(13,7))
ax.barh(y=co_name_list, width=growth_list, height=0.9, color=['#4bd659','#4bd659','#4bd659','#4bd659','#4bd659',
                                                             '#4bd659','#4bd659','#ed615c','#ed615c','#ed615c'],
       edgecolor='#713ae8')
for p in ax.patches:
    ax.annotate(f'{round(p.get_width(),2)}%', (p.get_width()+15, p.get_y() +0.3))
ax.set_xlabel('Percentage growth in stock price')
ax.set_ylabel('Name of companies')
ax.set_title("Growth in stock price over a period of 5 years")
plt.show()

# function to return top 10 sub dataframe
def subdataframe(df, tick):
    
    # top 10 ticks
    ticks = list(top10_s.index)
    
    assert tick in ticks, """Stock tick does not belong to top 10 stocks by trade volume, please try any of these:\n
    ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']"""
    
    ndf = new_df[new_df['ticks'] == tick]
    return ndf

ab = sp500Cmp_info_df[sp500Cmp_info_df['Industry'] == 'Software - Infrastructure']
ab=ab.sort_values(by='Marketcap',ascending=False)
ab=ab.head(6)
ab

company_dict = {
    'MSFT': 'Microsoft',
    'ORCL': 'Oracle Corporation',
    'ADBE': 'Adobe Inc.',
    'PANW': 'Palo Alto Networks',
    'SNPS': 'Synopsys Inc.',
    'CRWD': 'CrowdStrike Holdings'
}

def subdataframe(df, tick, *ticks):
    ticks_list = list(ticks)
    assert tick in ticks_list, f"Stock tick does not belong to the provided list of ticks: {ticks_list}"
    ndf = df[df['ticks'] == tick]
    return ndf

TechStocks = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Create individual DataFrames for each stock
MSFT_df = subdataframe(new_df, 'MSFT', *TechStocks)
ORCL_df = subdataframe(new_df, 'ORCL', *TechStocks)
ADBE_df = subdataframe(new_df, 'ADBE', *TechStocks)
PANW_df = subdataframe(new_df, 'PANW', *TechStocks)
SNPS_df = subdataframe(new_df, 'SNPS', *TechStocks)
CRWD_df = subdataframe(new_df, 'CRWD', *TechStocks)

# Apply the dailyfunc function to each DataFrame
MSFT_df = dailyfunc(MSFT_df)
ORCL_df = dailyfunc(ORCL_df)
ADBE_df = dailyfunc(ADBE_df)
PANW_df = dailyfunc(PANW_df)
SNPS_df = dailyfunc(SNPS_df)
CRWD_df = dailyfunc(CRWD_df)

background_gradient = LinearSegmentedColormap.from_list("", ["#212121", "#1A1D23", "#03055B"])
line_colors = ['#FFFF00', '#00BFFF', '#32CD32', '#FF00FF', '#FFA500', '#d62728']  

# Create the figure and axis with dark background
fig, ax = plt.subplots(figsize=(14, 7))
fig.patch.set_facecolor('#1A1D23')  # Set figure background color to match theme
ax.set_facecolor('#212121')  # Set axis background color

# Plot each stock's daily mean price with distinct colors and line widths for visibility on dark theme
ax.plot(MSFT_df['date'], MSFT_df['daily_mean'], label='Microsoft (MSFT)', color=line_colors[0], lw=1.4)
ax.plot(ORCL_df['date'], ORCL_df['daily_mean'], label='Oracle (ORCL)', color=line_colors[1], lw=1.4)
ax.plot(ADBE_df['date'], ADBE_df['daily_mean'], label='Adobe (ADBE)', color=line_colors[2], lw=1.4)
ax.plot(PANW_df['date'], PANW_df['daily_mean'], label='Palo Alto Networks (PANW)', color=line_colors[3], lw=1.4)
ax.plot(SNPS_df['date'], SNPS_df['daily_mean'], label='Synopsys (SNPS)', color=line_colors[4], lw=1.4)
ax.plot(CRWD_df['date'], CRWD_df['daily_mean'], label='CrowdStrike (CRWD)', color=line_colors[5], lw=1.4)

# Format the x-axis to display years only
ax.xaxis.set_major_locator(mdates.YearLocator())  # Place tick at the start of each year
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format to show only the year
plt.xticks(rotation=0, color='white')  # Make tick labels white for dark background

# Add title and labels with improved formatting
ax.set_title("Comparative Analysis of Tech Stock Prices Based on Mean Price", fontsize=16, fontweight='bold', color='white')
ax.set_ylabel("Daily Average Stock Price", fontsize=14, color='white')

# Add legend with a clear background
ax.legend(facecolor='#1A1D23', fontsize="medium", title="Tech Stocks", title_fontsize=13, labelcolor='white', edgecolor='white')

# Add gridlines for better readability, change to a softer color to match the dark theme
ax.grid(True, color='#444444')

# Show the plot
plt.tight_layout()
plt.show()

dataframes = [MSFT_df, ORCL_df, ADBE_df, PANW_df, SNPS_df, CRWD_df]
labels = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Set up the plot
plt.figure(figsize=(14, 8))

# Loop through each DataFrame to plot only KDE (no histograms)
for df, label in zip(dataframes, labels):
    # Plot only KDE for each DataFrame's 'daily return' with a specific color palette for differentiation
    sns.kdeplot(df['daily return'], label=label, linewidth=2)

# Add labels and title
plt.title('KDE of Daily Returns for Top 6 Stocks')
plt.xlabel('Daily Return')
plt.ylabel('Density')
plt.legend(title='Stock')

# Show the plot
plt.show()

dataframes = [MSFT_df, ORCL_df, ADBE_df, PANW_df, SNPS_df, CRWD_df]
labels = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Create a combined DataFrame for plotting
combined_df = pd.DataFrame()

# Loop through dataframes and calculate standard deviation of 'daily return'
std_devs = {}
for df, label in zip(dataframes, labels):
    # Create a temporary DataFrame with a 'Stock' column
    temp_df = df[['daily return']].copy()
    temp_df['Stock'] = label
    
    # Append to the combined DataFrame
    combined_df = pd.concat([combined_df, temp_df])
    
    # Calculate the standard deviation for this stock and store it
    std_devs[label] = temp_df['daily return'].std()

# Sort stocks by their standard deviation (volatility)
sorted_labels = sorted(std_devs, key=std_devs.get)

# Set up the plot
plt.figure(figsize=(12, 6))

# Create the box plot, sorted by volatility
sns.boxplot(x='Stock', y='daily return', data=combined_df, order=sorted_labels, palette="Set2")

# Add labels and title
plt.title('Box Plot of Daily Returns for Top 6 Stocks (Sorted by Volatility)')
plt.ylabel('Daily Return')

# Show the plot
plt.show()

means = []
std_devs = []

# Loop through dataframes and calculate mean and standard deviation for each stock
for df in dataframes:
    mean_return = df['daily return'].mean()
    std_dev = df['daily return'].std()
    means.append(mean_return)
    std_devs.append(std_dev)

# Define size and color for scatter plot
area = np.pi * 20  # Size of the scatter plot marker
colors = ['blue', 'green', 'red', 'purple', 'orange', 'brown']  # Colors for each stock

# Create a scatter plot
plt.figure(figsize=(12, 8))

# Plot each stock with a different color and annotate
for mean, std, label, color in zip(means, std_devs, labels, colors):
    plt.scatter(mean, std, s=area, c=color, label=label)
    plt.annotate(label, xy=(mean, std), xytext=(50, 50), textcoords='offset points',
                 ha='right', va='bottom', 
                 arrowprops=dict(arrowstyle='-', color=color, connectionstyle='arc3,rad=-0.3'))

# Add labels, title, and legend
plt.xlabel('Expected Return (Mean)')
plt.ylabel('Risk (Standard Deviation)')
plt.title('Scatter Plot of Risk vs Expected Return for Top 6 Stocks')

# Show the plot
plt.show()

SNPS_df

PANW_df

dataframes = [PANW_df, CRWD_df]
labels = ['PANW', 'CRWD']

PANW_df['date'] = pd.to_datetime(PANW_df['date'])
CRWD_df['date'] = pd.to_datetime(CRWD_df['date'])
PANW_df.set_index('date', inplace=True)
CRWD_df.set_index('date', inplace=True)

short_window = 50
long_window = 200

# Loop through companies and calculate moving averages
for company, label in zip([PANW_df, CRWD_df], ['PANW', 'CRWD']):
    company['Short MA'] = company['adj close'].rolling(window=short_window).mean()
    company['Long MA'] = company['adj close'].rolling(window=long_window).mean()
    
    # Detect Golden Cross and Death Cross
    company['Signal'] = 0.0  # Initialize signal column
    company['Signal'] = np.where(company['Short MA'] > company['Long MA'], 1.0, 0.0)
    company['Cross'] = company['Signal'].diff()

# Function to plot stock with Golden Cross and Death Cross
def plot_stock_with_cross(company, name, ax):
    ax.plot(company['adj close'], label='Adjusted Close', alpha=0.6)
    ax.plot(company['Short MA'], label=f'{short_window}-day MA', alpha=0.7)
    ax.plot(company['Long MA'], label=f'{long_window}-day MA', alpha=0.7)
    
    # Mark Golden Cross (1) and Death Cross (-1)
    ax.plot(company[company['Cross'] == 1].index, company['Short MA'][company['Cross'] == 1], '^', markersize=10, color='g', lw=0, label='Golden Cross')
    ax.plot(company[company['Cross'] == -1].index, company['Short MA'][company['Cross'] == -1], 'v', markersize=10, color='r', lw=0, label='Death Cross')
    
    ax.set_title(name)
    ax.legend()

    # Set major ticks to every year (from 2019 to 2024)
    ax.xaxis.set_major_locator(mdates.YearLocator())  # Every year
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format as 'Year'
    
    # Ensure x-axis spans from 2019 to 2024
    ax.set_xlim([pd.Timestamp('2019-01-01'), pd.Timestamp('2024-12-31')])

# Set up plot with subplots for PANW and CRWD
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))

# Plot for PANW and CRWD
plot_stock_with_cross(PANW_df, 'PANW', axes[0])
plot_stock_with_cross(CRWD_df, 'CRWD', axes[1])

plt.tight_layout()
plt.show()

check = CRWD_df[CRWD_df['Cross'] == -1]
check

symbols = ['GOOGL', 'MSFT','WMT','AMZN','BABA','ORCL','SAP','CRM']
holders_data=[]
for symbol in symbols:
    # Fetch the stock data
    stock = yf.Ticker(symbol)
    
    # Get historical market data
    hist = stock.history(start=start_date, end=end_date)
    
    # Get financial data
    info = stock.info
    financials = stock.financials
    balance_sheet = stock.balance_sheet
    cash_flow = stock.cashflow
    
    # Try to fetch major holders, handle if not available
    try:
        major_holders = stock.major_holders
    except:
        major_holders = None

    # Try to fetch institutional holders, handle if not available
    try:
        institutional_holders = stock.institutional_holders
    except:
        institutional_holders = None
        
    
    holders_data.append({
        'Stock Symbol': symbol,                     # Stock symbol
        'Major Holders': major_holders,             # Major holders data
        'Top 10 Institutional Holders': institutional_holders  # Top 10 institutional holders data
    })
    
    Holders_data_df=pd.DataFrame(holders_data)
    Holders_data_df.set_index('Stock Symbol', inplace=True)
        
        
    
    # Create a DataFrame with the desired information
    df = pd.DataFrame({
        'Date': hist.index,
        'Open': hist['Open'],
        'High': hist['High'],
        'Low': hist['Low'],
        'Close': hist['Close'],
        'Volume': hist['Volume'],
        'EBITDA': info.get('ebitda', None),
        'EPS': info.get('trailingEps', None),
        'PE_Ratio': info.get('trailingPE', None),
        'PB_Ratio': info.get('priceToBook', None),
        'Dividend_Yield': info.get('dividendYield', None),
        'Market_Cap': info.get('marketCap', None),
        'Revenue': financials.loc['Total Revenue'].values[0] if not financials.empty else None,
        'Net_Income': financials.loc['Net Income'].values[0] if not financials.empty else None,
        'Total_Assets': balance_sheet.loc['Total Assets'].values[0] if not balance_sheet.empty else None,
        'Total_Liabilities': balance_sheet.loc['Total Liabilities Net Minority Interest'].values[0] if not balance_sheet.empty else None,
        'Operating_Cash_Flow': cash_flow.loc['Operating Cash Flow'].values[0] if not cash_flow.empty else None,
    })
    print("Done")

Done
Done
Done
Done
Done
Done
Done
Done

def create_pie_charts(df):
    fig, axes = plt.subplots(1, 2, figsize=(12, 6))  # Create a 1x2 grid for the pie charts
    
    # Iterate through each stock symbol
    for idx, symbol in enumerate(df.index):
        # Extract top 10 institutional holders' data for the current stock symbol
        institutional_holders = df.loc[symbol, 'Top 10 Institutional Holders']
        
        # If the institutional holders' data is available
        if institutional_holders is not None:
            # Get the names of the top 10 holders and their corresponding positions (values)
            names = institutional_holders['Holder'].values[:10]
            values = institutional_holders['Shares'].values[:10]
            
            axes[idx].pie(values, labels=names, autopct='%1.1f%%', startangle=90)
            axes[idx].set_title(f'Top 10 Institutional Holders for {symbol}')
    
    plt.tight_layout()
    plt.show()

create_pie_charts(Holders_data_df)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[33], line 21
     18     plt.tight_layout()
     19     plt.show()
---> 21 create_pie_charts(Holders_data_df)

Cell In[33], line 15, in create_pie_charts(df)
     12         names = institutional_holders['Holder'].values[:10]
     13         values = institutional_holders['Shares'].values[:10]
---> 15         axes[idx].pie(values, labels=names, autopct='%1.1f%%', startangle=90)
     16         axes[idx].set_title(f'Top 10 Institutional Holders for {symbol}')
     18 plt.tight_layout()

IndexError: index 2 is out of bounds for axis 0 with size 2

symbols = ['GOOGL', 'MSFT','WMT','AMZN','BABA','ORCL','SAP','CRM']

file_paths = {
    'AMD': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\AMD PE Ratio 2010-2024.txt",
    'Apple': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\Apple PE Ratio 2010-2024.txt",
    'Microsoft': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\Microsoft PE Ratio 2010-2024.txt",
    'NVIDIA': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\NVIDIA PE Ratio 2010-2024.txt"
}

def clean_eps(value):
    if pd.isna(value) or value == '':
        return None
    return float(value.replace('$', ''))

dfs = {}

for stock, file_path in file_paths.items():
    df = pd.read_csv(file_path, sep=r'\s+', header=None, names=['Date', 'Price', 'EPS', 'PE ratio'])
    
    df['EPS'] = df['EPS'].apply(clean_eps)
    
    df['Price'] = pd.to_numeric(df['Price'], errors='coerce')
    df['PE ratio'] = pd.to_numeric(df['PE ratio'], errors='coerce')
    
    df['Date'] = pd.to_datetime(df['Date'])
    df.set_index('Date', inplace=True)
    
    df = df.iloc[1:]
    
    df.sort_index(inplace=True, ascending=False)
    
    dfs[stock] = df

# Extract the EPS columns from the DataFrames
eps_values = [df['EPS'] for df in dfs.values()]

fig = go.Figure()

# Add lines for each stock
for stock, eps in zip(dfs.keys(), eps_values):
    fig.add_trace(go.Scatter(x=eps.index, y=eps.values, name=stock, line=dict(width=2)))

# Set title and labels
fig.update_layout(
    title='EPS Over Time',
    xaxis_title='Date',
    yaxis_title='EPS',
    plot_bgcolor='#333333',
    paper_bgcolor='#333333',
    font_color='#ffffff'
)

# Set vibrant line colors
fig.update_traces(marker=dict(color=['#ff69b4', '#33cc33', '#6666ff', '#ffcc00']))

# Show the legend
fig.update_layout(showlegend=True)

fig.update_layout(
    width=600,  # Set the figure width (in pixels)
    height=300,  # Set the figure height (in pixels)
)

# Show the plot
fig.show()

pe_ratios = [df['PE ratio'] for df in dfs.values()]
stock_symbols = list(dfs.keys())

# Create the figure
fig = go.Figure()

# Add lines for all stocks, using the stock symbols from the dictionary keys
for i, stock in enumerate(stock_symbols):
    fig.add_trace(go.Scatter(x=pe_ratios[i].index, y=pe_ratios[i].values, name=stock, line=dict(width=2), visible=(i < 2)))

# Set title and labels
fig.update_layout(
    title='PE Ratio Over Time',
    xaxis_title='Date',
    yaxis_title='PE Ratio',
    plot_bgcolor='#333333',
    paper_bgcolor='#333333',
    font_color='#ffffff'
)

# Set vibrant line colors
fig.update_traces(marker=dict(color=['#ff69b4', '#33cc33', '#6666ff', '#ffcc00']))

# Add dropdown menu
buttons = [
    dict(label=f'{stock_symbols[0]} & {stock_symbols[3]}', method='update', args=[{'visible': [True, False, False, True]}]),
    dict(label=f'{stock_symbols[1]} & {stock_symbols[2]}', method='update', args=[{'visible': [False, True, True, False]}]),
    dict(label='All 4', method='update', args=[{'visible': [True, True, True, True]}])
]
fig.update_layout(updatemenus=[dict(buttons=buttons, direction='down', showactive=True)])

# Show the legend
fig.update_layout(showlegend=True)

# Show the plot
fig.show()

	Date	Symbol	Adj Close	Close	High	Low	Open	Volume
0	2010-01-04	MMM	44.016724	69.414719	69.774246	69.122070	69.473244	3640265.0
1	2010-01-05	MMM	43.741020	68.979935	69.590302	68.311035	69.230766	3405012.0
2	2010-01-06	MMM	44.361343	69.958191	70.735786	69.824417	70.133781	6301126.0
3	2010-01-07	MMM	44.393166	70.008362	70.033447	68.662209	69.665550	5346240.0
4	2010-01-08	MMM	44.705982	70.501671	70.501671	69.648827	69.974915	4073337.0

	Date	Symbol	Adj Close	Close	High	Low	Open	Volume
0	2019-09-16	MMM	115.520706	141.864548	143.152176	140.392975	143.152176	2941323.0
1	2019-09-17	MMM	114.431313	140.526749	140.944809	139.214050	140.267563	3248814.0
2	2019-09-18	MMM	114.002403	140.000000	140.510040	138.168900	140.142136	2507653.0
3	2019-09-19	MMM	113.750465	139.690628	141.145493	139.556854	139.958191	1941108.0
4	2019-09-20	MMM	113.539391	139.431442	141.446487	139.197327	139.991638	4423884.0
...	...	...	...	...	...	...	...	...
629248	2024-08-28	ZTS	182.910004	182.910004	184.610001	182.100006	182.100006	1485400.0
629249	2024-08-29	ZTS	182.889999	182.889999	184.710007	182.479996	183.910004	1359900.0
629250	2024-08-30	ZTS	183.490005	183.490005	184.220001	180.979996	183.089996	2328500.0
629251	2024-09-03	ZTS	183.570007	183.570007	184.750000	182.460007	182.710007	1680900.0
629252	2024-09-04	ZTS	187.330002	187.330002	187.500000	181.585007	183.190002	1846912.0

	Date	ticks	Adj Close	Close	High	Low	Open	Volume
0	2019-09-16	MMM	115.520706	141.864548	143.152176	140.392975	143.152176	2941323.0
1	2019-09-17	MMM	114.431313	140.526749	140.944809	139.214050	140.267563	3248814.0
2	2019-09-18	MMM	114.002403	140.000000	140.510040	138.168900	140.142136	2507653.0
3	2019-09-19	MMM	113.750465	139.690628	141.145493	139.556854	139.958191	1941108.0
4	2019-09-20	MMM	113.539391	139.431442	141.446487	139.197327	139.991638	4423884.0
...	...	...	...	...	...	...	...	...
629248	2024-08-28	ZTS	182.910004	182.910004	184.610001	182.100006	182.100006	1485400.0
629249	2024-08-29	ZTS	182.889999	182.889999	184.710007	182.479996	183.910004	1359900.0
629250	2024-08-30	ZTS	183.490005	183.490005	184.220001	180.979996	183.089996	2328500.0
629251	2024-09-03	ZTS	183.570007	183.570007	184.750000	182.460007	182.710007	1680900.0
629252	2024-09-04	ZTS	187.330002	187.330002	187.500000	181.585007	183.190002	1846912.0

	tick	Shortname	Sector	Industry
0	NVDA	NVIDIA Corporation	Technology	Semiconductors
1	TSLA	Tesla, Inc.	Consumer Cyclical	Auto Manufacturers
2	AAPL	Apple Inc.	Technology	Consumer Electronics
3	AMZN	Amazon.com, Inc.	Consumer Cyclical	Internet Retail
4	F	Ford Motor Company	Consumer Cyclical	Auto Manufacturers
5	AMD	Advanced Micro Devices, Inc.	Technology	Semiconductors
6	BAC	Bank of America Corporation	Financial Services	Banks - Diversified
7	T	AT&T Inc.	Communication Services	Telecom Services
8	AAL	American Airlines Group, Inc.	Industrials	Airlines
9	CCL	Carnival Corporation	Consumer Cyclical	Travel Services

	Exchange	Symbol	Shortname	Longname	Sector	Industry	Currentprice	Marketcap	Ebitda	Revenuegrowth	City	State	Country	Fulltimeemployees	Longbusinesssummary	Weight
1	NMS	MSFT	Microsoft Corporation	Microsoft Corporation	Technology	Software - Infrastructure	408.90	3039369887744	1.294330e+11	0.152	Redmond	WA	United States	228000.0	Microsoft Corporation develops and supports so...	0.059346
20	NYQ	ORCL	Oracle Corporation	Oracle Corporation	Technology	Software - Infrastructure	140.75	387887300608	2.122700e+10	0.033	Austin	TX	United States	159000.0	Oracle Corporation offers products and service...	0.007574
28	NMS	ADBE	Adobe Inc.	Adobe Inc.	Technology	Software - Infrastructure	575.25	255065849856	7.844000e+09	0.102	San Jose	CA	United States	29945.0	Adobe Inc., together with its subsidiaries, op...	0.004980
87	NMS	PANW	Palo Alto Networks, Inc.	Palo Alto Networks, Inc.	Technology	Software - Infrastructure	346.15	112533364736	9.601666e+08	0.121	Santa Clara	CA	United States	NaN	Palo Alto Networks, Inc. provides cybersecurit...	0.002197
130	NMS	SNPS	Synopsys, Inc.	Synopsys, Inc.	Technology	Software - Infrastructure	477.53	73355296768	1.652390e+09	0.127	Sunnyvale	CA	United States	20300.0	Synopsys, Inc. provides electronic design auto...	0.001432
149	NMS	CRWD	CrowdStrike Holdings, Inc.	CrowdStrike Holdings, Inc.	Technology	Software - Infrastructure	259.32	63566077952	1.874900e+08	0.317	Austin	TX	United States	9219.0	CrowdStrike Holdings, Inc. provides cybersecur...	0.001241

S&P 500 Latest Stocks Analysis¶

Analysis Breakdown¶

Data Cleaning¶

Continue with our Work For now on¶

Comparitive Analysis of of Top Tech Stocks in the Index¶

Volitatliy Analysis¶

Risk Analysis¶

Moving Average¶

Market and Equity Analysis of Stocks¶

Equity Distribution By Top 10 Institutional StakeHolders¶

Next we are Going to do some Analysis , Selecting do a Side by Side Comparision on Some Direct Alternates of Each Other as¶

	date	ticks	adj close	close	high	low	open	volume	daily return	daily_mean	co_name
540432	2019-09-16	SNPS	133.240005	133.240005	134.149994	132.529999	133.619995	1150600.0	-0.284381	133.384998	Synopsys Inc.
540433	2019-09-17	SNPS	136.699997	136.699997	136.880005	132.509995	133.050003	1028300.0	2.743325	134.785000	Synopsys Inc.
540434	2019-09-18	SNPS	136.500000	136.500000	137.070007	133.639999	136.660004	978700.0	-0.117082	135.967503	Synopsys Inc.
540435	2019-09-19	SNPS	137.130005	137.130005	138.729996	136.460007	136.669998	777300.0	0.336582	137.247501	Synopsys Inc.
540436	2019-09-20	SNPS	135.720001	135.720001	137.990005	135.630005	137.110001	1196700.0	-1.013784	136.612503	Synopsys Inc.
...	...	...	...	...	...	...	...	...	...	...	...
541678	2024-08-28	SNPS	509.980011	509.980011	523.799988	506.329987	522.909973	1082800.0	-2.472694	515.754990	Synopsys Inc.
541679	2024-08-29	SNPS	517.020020	517.020020	526.429993	513.429993	514.919983	932400.0	0.407837	517.949997	Synopsys Inc.
541680	2024-08-30	SNPS	519.580017	519.580017	529.000000	514.440002	525.530029	1233500.0	-1.132193	522.137512	Synopsys Inc.
541681	2024-09-03	SNPS	481.220001	481.220001	514.400024	479.089996	514.150024	2561000.0	-6.404750	497.215012	Synopsys Inc.
541682	2024-09-04	SNPS	477.529999	477.529999	483.399994	472.600006	475.658508	1892187.0	0.393453	477.297127	Synopsys Inc.

	date	ticks	adj close	close	high	low	open	volume	daily return	daily_mean	co_name
454113	2019-09-16	PANW	71.933334	71.933334	72.436668	69.243332	69.339996	4389000.0	3.740032	70.738333	Palo Alto Networks
454114	2019-09-17	PANW	70.896667	70.896667	71.823334	69.796669	71.496666	3570000.0	-0.839198	71.003334	Palo Alto Networks
454115	2019-09-18	PANW	70.063332	70.063332	71.000000	69.139999	70.883331	4560000.0	-1.156830	70.271666	Palo Alto Networks
454116	2019-09-19	PANW	69.629997	69.629997	70.266670	69.063332	69.720001	4064400.0	-0.129093	69.670000	Palo Alto Networks
454117	2019-09-20	PANW	69.416664	69.416664	70.860001	69.059998	69.666664	2958300.0	-0.358852	69.750832	Palo Alto Networks
...	...	...	...	...	...	...	...	...	...	...	...
455359	2024-08-28	PANW	350.440002	350.440002	352.779999	346.079987	349.470001	1653300.0	0.277564	349.692497	Palo Alto Networks
455360	2024-08-29	PANW	358.209991	358.209991	366.049988	352.220001	352.920013	2728500.0	1.498917	357.349998	Palo Alto Networks
455361	2024-08-30	PANW	362.720001	362.720001	362.970001	354.570007	358.420013	2988600.0	1.199706	359.670006	Palo Alto Networks
455362	2024-09-03	PANW	355.070007	355.070007	365.500000	352.950012	359.549988	2613800.0	-1.245997	358.267502	Palo Alto Networks
455363	2024-09-04	PANW	346.149994	346.149994	348.040009	338.089996	343.299988	3013721.0	0.830179	343.894997	Palo Alto Networks

	ticks	adj close	close	high	low	open	volume	daily return	daily_mean	co_name	Short MA	Long MA	Signal	Cross
date
2022-01-04	CRWD	189.190002	189.190002	197.445007	183.320007	195.889999	6503400.0	-3.420285	191.461254	CrowdStrike Holdings	235.3460	236.554925	0.0	-1.0
2024-09-03	CRWD	266.600006	266.600006	277.510010	265.290009	275.779999	5485800.0	-3.328738	271.295006	CrowdStrike Holdings	299.4642	300.198974	0.0	-1.0

S&P Index Comp	alternate 1	alternate 2	Stock Symbols
Google (Alphabet)	Microsoft		GOOG/GOOGL, MSFT
Walmart	Amazon	Alibaba	WMT, AMZN, BABA
Oracle	SAP	Salesforce	ORCL, SAP, CRM